Konrad 'ktoso' MalawskiGeeCON 2014 @ Kraków, PL

Konrad `@ktosopl` Malawski

need for asynchot pursuit

for Scalable apps

Konrad `ktoso` Malawski

Akka Team, Reactive Streams TCK

Konrad `@ktosopl` Malawski

akka.iotypesafe.comgeecon.org

Java.pl / KrakowScala.plsckrk.com / meetup.com/Paper-Cup @ London

GDGKrakow.pl lambdakrk.pl

Nice to meet you! Who are you guys?

Why scalability matters:

High Performance Software Development

For the majority of the time, high performance software development

is not about compiler hacks and bit twiddling.

It is about fundamental design principles that are key to doing any effective software development.

Martin Thompson

practicalperformanceanalyst.com/2015/02/17/getting-to-know-martin-thompson-...

Agenda• Why?

• Async and Synch basics / definitions

• Back-pressure applied

• Async where it matters: Scheduling

• How NOT to measure Latency

• Concurrent < lock-free < wait-free

• I/O: IO, AIO, NIO, Zero

• C10K: select, poll, epoll / kqueue

• Distributed Systems: Where Async is at Home

• Wrapping up and Q/A

Sync / Async Basics

Sync / Async

Sync / Async

Sync / Async

Sync / Async

Sync / Async

Sync / Async

Sync / Async

Sync / Async

Sync / Async

Asynchronous systems rely on buffering.

Asynchronous systems rely on buffering.

What if they overflow? How do you back-pressure?

Back-pressure? Example Without

Fast Publisher Slow Subscriber

Back-pressure? (example without)

Subscriber usually has some kind of buffer.

Back-pressure? (example without)

Back-pressure? (example without)

Back-pressure? (example without)

What if the buffer overflows?

Back-pressure? (example without)

Use bounded buffer, drop messages + require re-sending

Back-pressure? (example without)

Kernel does this!Routers do this!

(TCP)

Use bounded buffer, drop messages + require re-sending

Back-pressure “not needed” when:

speed(publisher) < speed(subscriber)

Back-pressure? Fast Subscriber, No Problem

No problem!

Back-pressure?NACKing is NOT enough.

Back-pressure? (example without)

Back-pressure? Example NACKingTelling the Publisher to slow down / stop sending…

Back-pressure? Example NACKingNACK did not make it in time,

because M was in-flight!

Back-pressure?Reactive-Streams

= “Dynamic Push/Pull”

Just push – not safe when Slow Subscriber

Just pull – too slow when Fast Subscriber

Back-pressure? RS: Dynamic Push/Pull

Solution:Dynamic adjustment

Back-pressure? RS: Dynamic Push/Pull

Just push – not safe when Slow Subscriber

Just pull – too slow when Fast Subscriber

Back-pressure? RS: Dynamic Push/PullSlow Subscriber sees it’s buffer can take 3 elements. Publisher will never blow up it’s buffer.

Back-pressure? RS: Dynamic Push/PullFast Publisher will send at-most 3 elements. This is pull-based-backpressure.

Back-pressure? RS: Dynamic Push/Pull

Fast Subscriber can issue more Request(n), before more data arrives!

Back-pressure? RS: Dynamic Push/PullFast Subscriber can issue more Request(n), before more data arrives.

Publisher can accumulate demand.

Back-pressure? RS: Accumulate demandPublisher accumulates total demand per subscriber.

Back-pressure? RS: Accumulate demandTotal demand of elements is safe to publish. Subscriber’s buffer will not overflow.

Back-pressure? Reactive StreamsMini in-line pitch

Specification and TCKreactive-streams.org

ImplementationsAkka Streams / Akka Http

Slick RxJava

ReactorRatPackVert.x

MongoDB driver

http://www.reactive-streams.org/announce-1.0.0

Highly parallel systemsEvent loops

Async where it matters:

Async where it matters: Scheduling

Async where it matters: Scheduling

Async where it matters: Scheduling

Async where it matters: Scheduling

Async where it matters: Scheduling

Async where it matters: Scheduling

Async where it matters: Scheduling

Async where it matters: Scheduling

Async where it matters: Scheduling

Scheduling (notice they grey sync call)

Scheduling (notice they grey sync call)

Scheduling (now with Async db call)

Scheduling (now with Async db call)

Scheduling (now with Async db call)

Scheduling (now with Async db call)

Latency

Latency

Time interval between

the stimulation and response.

Latency Quiz

Gil Tene style, see: “How NOT to Measure Latency”

Is 10s latency acceptable in your app? Is 200ms latency acceptable?

How about most responses within 200ms?So mostly 20ms and some 1 minute latencies is OK?

Do people die when we go above 200ms?

So 90% below 200ms, 99% bellow 1s, 99.99% below 2s?

Latency in the “real world”

Gil Tene style, see: “How NOT to Measure Latency”

“Our response time is 200ms average, stddev is around 60ms”

— a typical quote

Latency in the “real world”

Gil Tene style, see: “How NOT to Measure Latency”

“Our response time is 200ms average, stddev is around 60ms”

— a typical quote

Latency does NOT behave like normal distribution!

“So yeah, our 99,99%’ is…”

http://hdrhistogram.github.io/HdrHistogram/

Hiccups

Gil Tene style, see: “How NOT to Measure Latency”

Hiccups

Gil Tene style, see: “How NOT to Measure Latency”

Hiccups

Gil Tene style, see: “How NOT to Measure Latency”

Hiccups

Gil Tene style, see: “How NOT to Measure Latency”

Hiccups

Gil Tene style, see: “How NOT to Measure Latency”

Hiccups

Concurrent < lock-free < wait-free

Concurrent < lock-free < wait-free

Concurrent < lock-free < wait-free

Concurrent < lock-free < wait-free

Concurrent data structure

A locks; B tries lock... A writes;

A unlocks; B acquires the lock... B (finally) writes & unlocks

What can happen in concurrent data structures:

Concurrent < lock-free < wait-free

A locks; B tries lock... A writes;

A unlocks; B acquires the lock... B (finally) writes & unlocks

What can happen in concurrent data structures:

Moral? 1) Thread A is not very nice to B. 2) A lot of time is wasted.

Concurrent < lock-free < wait-freeW

asted time

Concurrent < lock-free < wait-less

Concurrency is NOT Parallelism.

Rob Pike - Concurrency is NOT Parallelism (video)

def offer(a: A): Boolean // returns on failuredef add(a: A): Unit // throws on failure

def put(a: A): Boolean // blocks until able to enqueue

Concurrent < lock-free < wait-free

concurrent data structure

< lock-free* data structure

* lock-free a.k.a. lockless

What lock-free programming looks like:

An algorithm is lock-free if it satisfies that:When the program threads are run sufficiently long,

at least one of the threads makes progress.

Concurrent < lock-free < wait-free

Concurrent < lock-free < wait-freeWhat can happen in concurrent data structures:

A tries to write; B tries to write; B wins!A tries to write; C tries to write; C wins!A tries to write; D tries to write; D wins!A tries to write; B tries to write; B wins!A tries to write; E tries to write; E wins!A tries to write; F tries to write; F wins!…

Moral? 1) Thread A is a complete loser. 2) Thread A eventually should be able to progress…

* Both versions are used: lock-free / lockless

class CASBackedQueue[A] { val _queue = new AtomicReference(Vector[A]())

// does not block, may spin though @tailrec final def put(a: A): Unit = { val queue = _queue.get val appended = queue :+ a

if (!_queue.compareAndSet(queue, appended)) put(a) }}

Concurrent < lock-free < wait-free

class CASBackedQueue[A] { val _queue = new AtomicReference(Vector[A]())

// does not block, may spin though @tailrec final def put(a: A): Unit = { val queue = _queue.get val appended = queue :+ a

if (!_queue.compareAndSet(queue, appended)) put(a) }}

Concurrent < lock-free < wait-free

class CASBackedQueue[A] { val _queue = new AtomicReference(Vector[A]())

// does not block, may spin though @tailrec final def put(a: A): Unit = { val queue = _queue.get val appended = queue :+ a

if (!_queue.compareAndSet(queue, appended)) put(a) }}

Concurrent < lock-free < wait-free

Concurrent < lock-free < wait-free

Concurrent < lock-free < wait-free

Concurrent < lock-free < wait-free

Concurrent < lock-free < wait-free

Concurrent < lock-free < wait-free

Concurrent < lock-free < wait-free

Concurrent < lock-free < wait-free

“concurrent” data structure

< lock-free* data structure

< wait-free data structure

* Both versions are used: lock-free / lockless

Concurrent < lock-free < wait-free

Simple, Fast, and Practical Non-Blocking and Blocking Concurrent Queue AlgorithmsMaged M. Michael Michael L. Scott

An algorithm is wait-free if every operation has a bound on the number of steps the algorithm will take before the

operation completes.

wait-free: j.u.c.ConcurrentLinkedQueue

Simple, Fast, and Practical Non-Blocking and Blocking Concurrent Queue AlgorithmsMaged M. Michael Michael L. Scott

public boolean offer(E e) { checkNotNull(e); final Node<E> newNode = new Node<E>(e);

for (Node<E> t = tail, p = t;;) { Node<E> q = p.next; if (q == null) { // p is last node if (p.casNext(null, newNode)) { // Successful CAS is the linearization point // for e to become an element of this queue, // and for newNode to become "live". if (p != t) // hop two nodes at a time casTail(t, newNode); // Failure is OK. return true; } // Lost CAS race to another thread; re-read next } else if (p == q) // We have fallen off list. If tail is unchanged, it // will also be off-list, in which case we need to // jump to head, from which all live nodes are always // reachable. Else the new tail is a better bet. p = (t != (t = tail)) ? t : head; else // Check for tail updates after two hops. p = (p != t && t != (t = tail)) ? t : q; }}

This is a modification of the Michael & Scott algorithm,adapted for a garbage-collected environment, with supportfor interior node deletion (to support remove(Object)). For explanation, read the paper.

I / O

IO / AIO

IO / AIO / NIO

IO / AIO / NIO / Zero

Synchronous I / O

— Havoc Pennington(HAL, GNOME, GConf, D-BUS, now Typesafe)

When I learned J2EE about 2008 with some of my desktop colleagues our reactions included something like:

”wtf is this sync IO crap,

where is the main loop?!” :-)

Interruption!CPU: User Mode / Kernel Mode

Kernels and CPUs

Kernels and CPUs

Kernels and CPUs

Kernels and CPUs

Kernels and CPUs

http://wiki.osdev.org/Context_Switching

Kernels and CPUs

[…] switching from user-level to kernel-level on a (2.8 GHz) P4 is 1348 cycles.

[…] Counting actual time, the P4 takes 481ns […]

http://wiki.osdev.org/Context_Switching

I / O

I / O

I / O

I / O

I / O

I / O

“Don’t worry. It only gets worse!”

I / O“Don’t worry.

It only gets worse!”

Same data in 3 buffers!4 mode switches!

Asynchronous I / O [Linux]

Linux AIO = JVM NIO

Asynchronous I / O [Linux]

NewIO… since 2004!(No-one calls it “new” any more)

Linux AIO = JVM NIO

Asynchronous I / O [Linux]

Less time wasted waiting. Same amount of buffer copies.

ZeroCopy = sendfile [Linux]

“Work smarter. Not harder.”

http://fourhourworkweek.com/

ZeroCopy = sendfile [Linux]

ZeroCopy = sendfile [Linux]

Data never leaves kernel mode!

ZeroCopy…

C10K and beyond

C10K and beyond

“10.000 concurrent connections”

Not a new problem, pretty old actually: ~12 years old.

http://www.kegel.com/c10k.html

C10K and beyond

It’s not about performance.It’s about scalability.

These are orthogonal things.

Threading differences: apache httpd / nginx / akka

(1 thread = 1 request) == Very heavy

select/poll

C10K – poll

C10K – poll

C10K – poll

epoll

C10K – epoll [Linux]

C10K – epoll [Linux]

C10K – epoll [Linux]

C10K – epoll [Linux]

C10K – epoll [Linux]

C10K

O(n) is a no-go for epic scalability.

C10K

O(n) is a no-go for epic scalability.

State of Linux scheduling: O(n) ⇒ O(1) ⇒ CFS (O(1)/ O(log n))

And Socket selection: Select/Poll O(n) ⇒ EPoll (O(1))

C10K

O(n) is a no-go for epic scalability.

State of Linux scheduling: O(n) ⇒ O(1) ⇒ CFS (O(1))

And Socket selection: Select/Poll O(n) ⇒ EPoll (O(1))

O(1) IS a go for epic scalability.Moral:

Distributed Systems

Distributed Systems“… in which the failure of a computer you didn't even know existed canrender your own computer unusable.”

— Leslie Lamport

http://research.microsoft.com/en-us/um/people/lamport/pubs/distributed-system.txt

Distributed SystemsThe bigger the system, the more “random” latency / failure noise.

Embrace instead of hiding it.

Distributed SystemsBackup Requests

Backup requests

A technique for fighting “long tail latencies”.By issuing duplicated work, when SLA seems in danger.

Backup requests

Backup requests

Backup requests - send

Backup requests - send

Backup requests - send

Backup requests

Avg Std dev 95%ile 99%ile 99.9%ile

No backups 33 ms 1524 ms 24 ms 52 ms 994 ms

After 10ms 14 ms 4 ms 20 ms 23 ms 50 ms

After 50ms 16 ms 12 ms 57 ms 63 ms 68 ms

Jeff Dean - Achieving Rapid Response Times in Large Online Services Peter Bailis - Doing Redundant Work to Speed Up Distributed Queries

Akka - Krzysztof Janosz @ Akkathon, Kraków - TailChoppingRouter (docs, pr)

Distributed SystemsCombined Requests

Combined requests

A technique for avoiding duplicated work.By aggregating requests, possibly increasing latency.

Combined requests

A technique for avoiding duplicated work.By aggregating requests, possibly increasing latency.

“Wat? Why would I increase latency!?”

Combined requests

Combined requests

Combined requests

Combined requests

Combined requests

Combined requests

World of Tradeoffs

Combined requests with backpressure

reactive-streams.org

Timer can be replaced with back-pressure.

Wrapping up

Wrapping up

• Someone has to bite the bullet though!

• We’re all running on real hardware.

• We do it so you don’t have to.

Summing up the goal of this talk:

• Keep your apps pure and simple

• Be aware of internals

• Use libraries which apply these techniques

• Maybe indeed

• Messaging all the way!

These techniques are hard to get right, yet:

Links• akka.io • reactive-streams.org• akka-user

• Gil Tene - How NOT to measure latency, 2013• Jeff Dean @ Velocity 2014• Alan Bateman, Jeanfrancois Arcand (Sun) Async IO Tips @ JavaOne• http://linux.die.net/man/2/select• http://linux.die.net/man/2/poll• http://linux.die.net/man/4/epoll• giltene/jHiccup• Linux Journal: ZeroCopy I, Dragan Stancevis 2013

• Last slide car picture: http://actu-moteurs.com/sprint/gt-tour/jean-philippe-belloc-un-beau-challenge-avec-le-akka-asp-team/2000

Links• http://wiki.osdev.org/Context_Switching• CppCon: Herb Sutter "Lock-Free Programming (or, Juggling Razor Blades)" • http://www.infoq.com/presentations/reactive-services-scale• Gil Tene’s HdrHistogram.org

• http://hdrhistogram.github.io/HdrHistogram/plotFiles.html• Rob Pike - Concurrency is NOT Parallelism (video)• Brendan Gregg - Systems Performance: Enterprise and the Cloud (book)• http://psy-lob-saw.blogspot.com/2015/02/hdrhistogram-better-latency-capture.html• Jeff Dean, Luiz Andre Barroso - The Tail at Scale (whitepaper, ACM)• http://highscalability.com/blog/2012/3/12/google-taming-the-long-latency-tail-when-

more-machines-equal.html• http://www.ulduzsoft.com/2014/01/select-poll-epoll-practical-difference-for-system-

architects/• Marcus Lagergren - Oracle JRockit: The Definitive Guide (book)• http://mechanical-sympathy.blogspot.com/2013/08/lock-based-vs-lock-free-

concurrent.html• Handling of Asynchronous Events - http://www.win.tue.nl/~aeb/linux/lk/lk-12.html• http://www.kegel.com/c10k.html

Links• www.reactivemanifesto.org/• Seriously the only right way to micro benchmark on the JVM:

• JMH openjdk.java.net/projects/code-tools/jmh/• JMH for Scala: https://github.com/ktoso/sbt-jmh

• http://www.ibm.com/developerworks/library/l-async/• http://lse.sourceforge.net/io/aio.html• https://code.google.com/p/kernel/wiki/AIOUserGuide• ShmooCon: C10M - Defending the Internet At Scale (Robert Graham)

• http://blog.erratasec.com/2013/02/scalability-its-question-that-drives-us.html#.VO6E11PF8SM

• User-level threads....... with threads. - Paul Turner @ Linux Plumbers Conf 2013• Jan Pustelnik’s talk yesterday: https://github.com/gosubpl/sortbench

Special thanks to:

• Aleksey Shipilëv• Andrzej Grzesik• Gil Tene• Kirk Pepperdine• Łukasz Dubiel• Marcus Lagergren• Martin Thompson• Mateusz Dymczyk• Nitsan Wakart

Thanks guys, you’re awesome.

alphabetically, mostly

• Peter Lawrey• Richard Warburton• Roland Kuhn• Sergey Kuksenko• Steve Poole• Viktor Klang a.k.a. √• Antoine de Saint Exupéry :-)• and the entire Akka Team• the Mechanical Sympathy Mailing List

Learn more at:

• SCKRK.com –

• KrakowScala.pl – Kraków Scala User Group

• LambdaKRK.pl – Lambda Lounge Kraków

• GeeCON.org – awesome “all around the JVM” conference, [Kraków, Poznań, Prague, and more…!]

Software Craftsmanship KrakówComputer Science Whitepaper Reading Club Kraków

Questions?

ktoso @ typesafe.com twitter: ktosopl

github: ktosoteam blog: letitcrash.com

home: akka.io

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